Current ventilation systems require control by a clinician, who inputs control values for the ventilator, including ventilation rate and gas values. For example, during surgery and in an intensive care unit a patient is ventilated mechanically by a mechanical ventilator that is controlled by a clinician. In such applications, end tidal CO2 (EtCO2) is often measured to evaluate ventilation adequacy and to supervise patient status. Changes in EtCO2 can be an indication of metabolic and/or hemodynamic changes in a patient, and thus EtCO2 is a valuable monitoring parameter to clinicians.
Currently available systems that require clinician control are prone to user error. Thus, it is desirable to create an automated ventilation system that eliminates the requirement of clinician control. If administered, properly, automatic ventilation control can eliminate user error and provide a safer ventilation control to a patient. However, choosing the right control variables and effectuating the automatic control algorithms is challenging, because the human respiratory system is a complicated system with many variables that must be accounted for.